Recent advancements in computer technology have shifted human experiences from the physical to the virtual realm. However, this transition has also brought about an increase in malicious software, or malware, used in cyberattacks. These malware variants continuously evolve, employing sophisticated techniques like advanced packing and obfuscation, posing challenges for traditional detection methods. To address this, an approach called Safeguarding Android Devices through Deep learning against Malware is proposed. This technique integrates data pre-processing and ensemble learning, leveraging two machine learning models—Random forest Classifier (RFC), Extra Tree Classifier (ETC), along with deep learning methods Convolutional neural network (CNN)and Artificial neural network (ANN). Through comprehensive experimental analysis, the CNN deep learning technique demonstrates superior performance compared to existing methods in detecting Android malware.